Self-Supervised Reversed Image Signal Processing via Reference-Guided Dynamic Parameter Selection

• URL: http://arxiv.org/abs/2303.13916v1
• Date: Fri, 24 Mar 2023 11:12:05 GMT
• Title: Self-Supervised Reversed Image Signal Processing via Reference-Guided Dynamic Parameter Selection
• Authors: Junji Otsuka, Masakazu Yoshimura, Takeshi Ohashi
• Abstract summary: We propose a self-supervised reversed ISP method that does not require metadata and paired images. The proposed method converts a RGB image into a RAW-like image taken in the same environment with the same sensor as a reference RAW image. We show that the proposed method is able to learn various reversed ISPs with comparable accuracy to other state-of-the-art supervised methods.
• Score: 1.1602089225841632
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unprocessed sensor outputs (RAW images) potentially improve both low-level and high-level computer vision algorithms, but the lack of large-scale RAW image datasets is a barrier to research. Thus, reversed Image Signal Processing (ISP) which converts existing RGB images into RAW images has been studied. However, most existing methods require camera-specific metadata or paired RGB and RAW images to model the conversion, and they are not always available. In addition, there are issues in handling diverse ISPs and recovering global illumination. To tackle these limitations, we propose a self-supervised reversed ISP method that does not require metadata and paired images. The proposed method converts a RGB image into a RAW-like image taken in the same environment with the same sensor as a reference RAW image by dynamically selecting parameters of the reversed ISP pipeline based on the reference RAW image. The parameter selection is trained via pseudo paired data created from unpaired RGB and RAW images. We show that the proposed method is able to learn various reversed ISPs with comparable accuracy to other state-of-the-art supervised methods and convert unknown RGB images from COCO and Flickr1M to target RAW-like images more accurately in terms of pixel distribution. We also demonstrate that our generated RAW images improve performance on real RAW image object detection task.

Related papers

• BSRAW: Improving Blind RAW Image Super-Resolution [63.408484584265985]
  We tackle blind image super-resolution in the RAW domain. We design a realistic degradation pipeline tailored specifically for training models with raw sensor data. Our BSRAW models trained with our pipeline can upscale real-scene RAW images and improve their quality.
  arXiv  Detail & Related papers  (2023-12-24T14:17:28Z)
• Raw Image Reconstruction with Learned Compact Metadata [61.62454853089346]
  We propose a novel framework to learn a compact representation in the latent space serving as the metadata in an end-to-end manner. We show how the proposed raw image compression scheme can adaptively allocate more bits to image regions that are important from a global perspective.
  arXiv  Detail & Related papers  (2023-02-25T05:29:45Z)
• Efficient Visual Computing with Camera RAW Snapshots [41.9863557302409]
  Conventional cameras capture image irradiance on a sensor and convert it to RGB images using an image signal processor (ISP) One can argue that since RAW images contain all the captured information, the conversion of RAW to RGB using an ISP is not necessary for visual computing. We propose a novel $rho$-Vision framework to perform high-level semantic understanding and low-level compression using RAW images.
  arXiv  Detail & Related papers  (2022-12-15T12:54:21Z)
• Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report [109.2135194765743]
  This paper introduces the AIM 2022 Challenge on Reversed Image Signal Processing and RAW Reconstruction. We aim to recover raw sensor images from the corresponding RGBs without metadata and, by doing this, "reverse" the ISP transformation.
  arXiv  Detail & Related papers  (2022-10-20T10:43:53Z)
• Transform your Smartphone into a DSLR Camera: Learning the ISP in the Wild [159.71025525493354]
  We propose a trainable Image Signal Processing framework that produces DSLR quality images given RAW images captured by a smartphone. To address the color misalignments between training image pairs, we employ a color-conditional ISP network and optimize a novel parametric color mapping between each input RAW and reference DSLR image.
  arXiv  Detail & Related papers  (2022-03-20T20:13:59Z)
• Model-Based Image Signal Processors via Learnable Dictionaries [6.766416093990318]
  Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP) Recent approaches have attempted to bridge this gap by estimating the RGB to RAW mapping. We present a novel hybrid model-based and data-driven ISP that is both learnable and interpretable.
  arXiv  Detail & Related papers  (2022-01-10T08:36:10Z)
• Towards Low Light Enhancement with RAW Images [101.35754364753409]
  We make the first benchmark effort to elaborate on the superiority of using RAW images in the low light enhancement. We develop a new evaluation framework, Factorized Enhancement Model (FEM), which decomposes the properties of RAW images into measurable factors. A RAW-guiding Exposure Enhancement Network (REENet) is developed, which makes trade-offs between the advantages and inaccessibility of RAW images in real applications.
  arXiv  Detail & Related papers  (2021-12-28T07:27:51Z)
• Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision [76.41657124981549]
  This paper presents a joint learning model for image alignment and RAW-to-sRGB mapping. Experiments show that our method performs favorably against state-of-the-arts on ZRR and SR-RAW datasets.
  arXiv  Detail & Related papers  (2021-08-18T12:41:36Z)
• Invertible Image Signal Processing [42.109752151834456]
  Invertible Image Signal Processing (InvISP) pipeline enables rendering visually appealing sRGB images. We can reconstruct realistic RAW data instead of synthesizing RAW data from sRGB images without any memory overhead.
  arXiv  Detail & Related papers  (2021-03-28T06:30:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.